library("FRESA.CAD")
library(readxl)
library(igraph)
library(umap)
library(tsne)
library(entropy)
op <- par(no.readonly = TRUE)
pander::panderOptions('digits', 3)
pander::panderOptions('table.split.table', 400)
pander::panderOptions('keep.trailing.zeros',TRUE)
Data from the speech features
pd_speech_features <- as.data.frame(read_excel("~/GitHub/FCA/Data/pd_speech_features.xlsx",sheet = "pd_speech_features", range = "A2:ACB758"))
Each subject had three repeated observations. Here I’ll use the average of the three experiments per subject.
rep1Parkison <- subset(pd_speech_features,RID==1)
rownames(rep1Parkison) <- rep1Parkison$id
rep1Parkison$id <- NULL
rep1Parkison$RID <- NULL
rep1Parkison[,1:ncol(rep1Parkison)] <- sapply(rep1Parkison,as.numeric)
rep2Parkison <- subset(pd_speech_features,RID==2)
rownames(rep2Parkison) <- rep2Parkison$id
rep2Parkison$id <- NULL
rep2Parkison$RID <- NULL
rep2Parkison[,1:ncol(rep2Parkison)] <- sapply(rep2Parkison,as.numeric)
rep3Parkison <- subset(pd_speech_features,RID==3)
rownames(rep3Parkison) <- rep3Parkison$id
rep3Parkison$id <- NULL
rep3Parkison$RID <- NULL
rep3Parkison[,1:ncol(rep3Parkison)] <- sapply(rep3Parkison,as.numeric)
whof <- !(colnames(rep1Parkison) %in% c("gender","class"));
avgParkison <- rep1Parkison;
avgParkison[,whof] <- (rep1Parkison[,whof] + rep2Parkison[,whof] + rep3Parkison[,whof])/3
signedlog <- function(x) { return (sign(x)*log(abs(1.0e12*x)+1.0))}
whof <- !(colnames(avgParkison) %in% c("gender","class"));
avgParkison[,whof] <- signedlog(avgParkison[,whof])
studyName <- "Parkinsons"
dataframe <- avgParkison
outcome <- "class"
TopVariables <- 10
thro <- 0.80
cexheat = 0.15
Some libraries
library(psych)
library(whitening)
library("vioplot")
library("rpart")
pander::pander(c(rows=nrow(dataframe),col=ncol(dataframe)-1))
| rows | col |
|---|---|
| 252 | 753 |
pander::pander(table(dataframe[,outcome]))
| 0 | 1 |
|---|---|
| 64 | 188 |
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
largeSet <- length(varlist) > 1500
Scaling and removing near zero variance columns and highly co-linear(r>0.99999) columns
### Some global cleaning
sdiszero <- apply(dataframe,2,sd) > 1.0e-16
dataframe <- dataframe[,sdiszero]
varlist <- colnames(dataframe)[colnames(dataframe) != outcome]
tokeep <- c(as.character(correlated_Remove(dataframe,varlist,thr=0.99999)),outcome)
dataframe <- dataframe[,tokeep]
varlist <- colnames(dataframe)
varlist <- varlist[varlist != outcome]
iscontinous <- sapply(apply(dataframe,2,unique),length) > 5 ## Only variables with enough samples
dataframeScaled <- FRESAScale(dataframe,method="OrderLogit")$scaledData
numsub <- nrow(dataframe)
if (numsub > 1000) numsub <- 1000
if (!largeSet)
{
hm <- heatMaps(data=dataframeScaled[1:numsub,],
Outcome=outcome,
Scale=TRUE,
hCluster = "row",
xlab="Feature",
ylab="Sample",
srtCol=45,
srtRow=45,
cexCol=cexheat,
cexRow=cexheat
)
par(op)
}
The heat map of the data
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
#cormat <- Rfast::cora(as.matrix(dataframe[,varlist]),large=TRUE)
cormat <- cor(dataframe[,varlist],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Original Correlation",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.9999951
DEdataframe <- IDeA(dataframe,verbose=TRUE,thr=thro)
#>
#> Included: 744 , Uni p: 0.01657441 , Uncorrelated Base: 192 , Outcome-Driven Size: 0 , Base Size: 192
#>
#>
1 <R=1.000,r=0.975,N= 363>, Top: 78( 2 )[ 1 : 78 Fa= 77 : 0.975 ]( 77 , 204 , 0 ),<|>Tot Used: 281 , Added: 204 , Zero Std: 0 , Max Cor: 1.000
#>
2 <R=1.000,r=0.975,N= 363>, Top: 20( 4 )[ 1 : 20 Fa= 96 : 0.975 ]( 20 , 59 , 77 ),<|>Tot Used: 334 , Added: 59 , Zero Std: 0 , Max Cor: 1.000
#>
3 <R=1.000,r=0.975,N= 363>, Top: 12( 1 )[ 1 : 12 Fa= 108 : 0.975 ]( 12 , 21 , 96 ),<|>Tot Used: 349 , Added: 21 , Zero Std: 0 , Max Cor: 1.000
#>
4 <R=1.000,r=0.950,N= 195>, Top: 73( 5 )[ 1 : 73 Fa= 144 : 0.950 ]( 72 , 94 , 108 ),<|>Tot Used: 417 , Added: 94 , Zero Std: 0 , Max Cor: 0.991
#>
5 <R=0.991,r=0.945,N= 195>, Top: 23( 1 )[ 1 : 23 Fa= 153 : 0.945 ]( 23 , 27 , 144 ),<|>Tot Used: 426 , Added: 27 , Zero Std: 0 , Max Cor: 0.965
#>
6 <R=0.965,r=0.932,N= 195>, Top: 30( 1 )[ 1 : 30 Fa= 161 : 0.932 ]( 30 , 37 , 153 ),<|>Tot Used: 442 , Added: 37 , Zero Std: 0 , Max Cor: 0.950
#>
7 <R=0.950,r=0.925,N= 195>, Top: 13( 1 )[ 1 : 13 Fa= 166 : 0.925 ]( 13 , 13 , 161 ),<|>Tot Used: 448 , Added: 13 , Zero Std: 0 , Max Cor: 0.924
#>
8 <R=0.924,r=0.862,N= 173>, Top: 63( 2 )[ 1 : 63 Fa= 189 : 0.862 ]( 62 , 85 , 166 ),<|>Tot Used: 478 , Added: 85 , Zero Std: 0 , Max Cor: 0.981
#>
9 <R=0.981,r=0.891,N= 173>, Top: 7( 1 )[ 1 : 7 Fa= 192 : 0.891 ]( 7 , 7 , 189 ),<|>Tot Used: 481 , Added: 7 , Zero Std: 0 , Max Cor: 0.890
#>
10 <R=0.890,r=0.800,N= 180>, Top: 62( 6 )[ 1 : 62 Fa= 210 : 0.800 ]( 59 , 91 , 192 ),<|>Tot Used: 507 , Added: 91 , Zero Std: 0 , Max Cor: 0.930
#>
11 <R=0.930,r=0.815,N= 180>, Top: 12( 1 )[ 1 : 12 Fa= 215 : 0.815 ]( 12 , 14 , 210 ),<|>Tot Used: 511 , Added: 14 , Zero Std: 0 , Max Cor: 0.914
#>
12 <R=0.914,r=0.800,N= 9>, Top: 4( 1 )[ 1 : 4 Fa= 216 : 0.800 ]( 4 , 5 , 215 ),<|>Tot Used: 511 , Added: 5 , Zero Std: 0 , Max Cor: 0.799
#>
13 <R=0.799,r=0.800,N= 9>
#>
[ 13 ], 0.7994489 Decor Dimension: 511 Nused: 511 . Cor to Base: 133 , ABase: 11 , Outcome Base: 0
#>
varlistc <- colnames(DEdataframe)[colnames(DEdataframe) != outcome]
pander::pander(sum(apply(dataframe[,varlist],2,var)))
57178
pander::pander(sum(apply(DEdataframe[,varlistc],2,var)))
55983
pander::pander(entropy(discretize(unlist(dataframe[,varlist]), 256)))
4.68
pander::pander(entropy(discretize(unlist(DEdataframe[,varlistc]), 256)))
2.45
if (!largeSet)
{
par(cex=0.6,cex.main=0.85,cex.axis=0.7)
UPSTM <- attr(DEdataframe,"UPSTM")
gplots::heatmap.2(1.0*(abs(UPSTM)>0),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Decorrelation matrix",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Beta|>0",
xlab="Output Feature", ylab="Input Feature")
par(op)
}
if (!largeSet)
{
cormat <- cor(DEdataframe[,varlistc],method="pearson")
cormat[is.na(cormat)] <- 0
gplots::heatmap.2(abs(cormat),
trace = "none",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "Correlation after IDeA",
cexRow = cexheat,
cexCol = cexheat,
srtCol=45,
srtRow=45,
key.title=NA,
key.xlab="|Pearson Correlation|",
xlab="Feature", ylab="Feature")
par(op)
diag(cormat) <- 0
print(max(abs(cormat)))
}
[1]
0.7994489
if (nrow(dataframe) < 1000)
{
classes <- unique(dataframe[1:numsub,outcome])
raincolors <- rainbow(length(classes))
names(raincolors) <- classes
datasetframe.umap = umap(scale(dataframe[1:numsub,varlist]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: Original",t='n')
text(datasetframe.umap$layout,labels=dataframe[1:numsub,outcome],col=raincolors[dataframe[1:numsub,outcome]+1])
}
if (nrow(dataframe) < 1000)
{
datasetframe.umap = umap(scale(DEdataframe[1:numsub,varlistc]),n_components=2)
plot(datasetframe.umap$layout,xlab="U1",ylab="U2",main="UMAP: After IDeA",t='n')
text(datasetframe.umap$layout,labels=DEdataframe[1:numsub,outcome],col=raincolors[DEdataframe[1:numsub,outcome]+1])
}
univarRAW <- uniRankVar(varlist,
paste(outcome,"~1"),
outcome,
dataframe,
rankingTest="AUC")
100 : std_MFCC_2nd_coef 200 : app_entropy_log_3_coef 300 :
app_LT_TKEO_mean_7_coef 400 : tqwt_entropy_log_dec_15 500 :
tqwt_medianValue_dec_7
600 : tqwt_stdValue_dec_35 700 : tqwt_skewnessValue_dec_27
univarDe <- uniRankVar(varlistc,
paste(outcome,"~1"),
outcome,
DEdataframe,
rankingTest="AUC",
)
100 : std_MFCC_2nd_coef 200 : La_app_entropy_log_3_coef 300 :
La_app_LT_TKEO_mean_7_coef 400 : La_tqwt_entropy_log_dec_15 500 :
tqwt_medianValue_dec_7
600 : La_tqwt_stdValue_dec_35 700 : tqwt_skewnessValue_dec_27
univariate_columns <- c("caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC")
##topfive
topvar <- c(1:length(varlist)) <= TopVariables
pander::pander(univarRAW$orderframe[topvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| std_delta_delta_log_energy | 23.4 | 0.469 | 22.8 | 0.461 | 0.653 | 0.798 |
| std_delta_log_energy | 24.3 | 0.477 | 23.8 | 0.441 | 0.634 | 0.794 |
| std_9th_delta_delta | 23.6 | 0.242 | 23.4 | 0.171 | 0.746 | 0.787 |
| std_8th_delta_delta | 23.7 | 0.240 | 23.4 | 0.150 | 0.725 | 0.780 |
| std_7th_delta_delta | 23.7 | 0.261 | 23.5 | 0.188 | 0.931 | 0.776 |
| tqwt_entropy_log_dec_12 | -39.6 | 0.239 | -39.4 | 0.240 | 0.887 | 0.770 |
| std_6th_delta_delta | 23.8 | 0.277 | 23.5 | 0.172 | 0.945 | 0.768 |
| std_8th_delta | 24.4 | 0.245 | 24.2 | 0.163 | 0.981 | 0.767 |
| std_9th_delta | 24.4 | 0.249 | 24.1 | 0.185 | 0.398 | 0.764 |
| tqwt_entropy_shannon_dec_12 | 30.3 | 1.993 | 32.1 | 1.703 | 0.196 | 0.763 |
topLAvar <- univarDe$orderframe$Name[str_detect(univarDe$orderframe$Name,"La_")]
topLAvar <- unique(c(univarDe$orderframe$Name[topvar],topLAvar[1:as.integer(TopVariables/2)]))
finalTable <- univarDe$orderframe[topLAvar,univariate_columns]
theLaVar <- rownames(finalTable)[str_detect(rownames(finalTable),"La_")]
pander::pander(univarDe$orderframe[topLAvar,univariate_columns])
| caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | |
|---|---|---|---|---|---|---|
| std_delta_log_energy | 24.335 | 0.477 | 23.810 | 0.441 | 6.34e-01 | 0.794 |
| std_8th_delta_delta | 23.660 | 0.240 | 23.428 | 0.150 | 7.25e-01 | 0.780 |
| tqwt_entropy_log_dec_12 | -39.634 | 0.239 | -39.390 | 0.240 | 8.87e-01 | 0.770 |
| La_tqwt_entropy_log_dec_28 | -0.633 | 0.430 | -0.819 | 0.273 | 1.25e-07 | 0.758 |
| La_std_2nd_delta | 0.462 | 0.132 | 0.329 | 0.144 | 7.54e-01 | 0.754 |
| mean_MFCC_2nd_coef | 21.360 | 18.112 | 1.716 | 27.881 | 4.61e-07 | 0.753 |
| La_tqwt_energy_dec_33 | 0.745 | 0.372 | 1.217 | 0.680 | 8.01e-01 | 0.736 |
| La_tqwt_kurtosisValue_dec_33 | 6.360 | 0.407 | 5.975 | 0.553 | 1.62e-01 | 0.736 |
| tqwt_kurtosisValue_dec_18 | 28.598 | 0.288 | 28.395 | 0.144 | 9.92e-01 | 0.734 |
| La_apq11Shimmer | 2.150 | 0.161 | 2.031 | 0.133 | 4.19e-01 | 0.734 |
dc <- getLatentCoefficients(DEdataframe)
fscores <- attr(DEdataframe,"fscore")
theSigDc <- dc[theLaVar]
names(theSigDc) <- NULL
theSigDc <- unique(names(unlist(theSigDc)))
theFormulas <- dc[rownames(finalTable)]
deFromula <- character(length(theFormulas))
names(deFromula) <- rownames(finalTable)
pander::pander(c(mean=mean(sapply(dc,length)),total=length(dc),fraction=length(dc)/(ncol(dataframe)-1)))
| mean | total | fraction |
|---|---|---|
| 2.57 | 469 | 0.63 |
allSigvars <- names(dc)
dx <- names(deFromula)[1]
for (dx in names(deFromula))
{
coef <- theFormulas[[dx]]
cname <- names(theFormulas[[dx]])
names(cname) <- cname
for (cf in names(coef))
{
if (cf != dx)
{
if (coef[cf]>0)
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("+ %5.3f*%s",coef[cf],cname[cf]))
}
else
{
deFromula[dx] <- paste(deFromula[dx],
sprintf("%5.3f*%s",coef[cf],cname[cf]))
}
}
}
}
finalTable <- rbind(finalTable,univarRAW$orderframe[theSigDc[!(theSigDc %in% rownames(finalTable))],univariate_columns])
orgnamez <- rownames(finalTable)
orgnamez <- str_remove_all(orgnamez,"La_")
finalTable$RAWAUC <- univarRAW$orderframe[orgnamez,"ROCAUC"]
finalTable$DecorFormula <- deFromula[rownames(finalTable)]
finalTable$fscores <- fscores[rownames(finalTable)]
Final_Columns <- c("DecorFormula","caseMean","caseStd","controlMean","controlStd","controlKSP","ROCAUC","RAWAUC","fscores")
finalTable <- finalTable[order(-finalTable$ROCAUC),]
pander::pander(finalTable[,Final_Columns])
| DecorFormula | caseMean | caseStd | controlMean | controlStd | controlKSP | ROCAUC | RAWAUC | fscores | |
|---|---|---|---|---|---|---|---|---|---|
| std_delta_log_energy | 24.335 | 0.477 | 23.810 | 0.441 | 6.34e-01 | 0.794 | 0.794 | 2 | |
| std_8th_delta_delta | 23.660 | 0.240 | 23.428 | 0.150 | 7.25e-01 | 0.780 | 0.780 | 6 | |
| tqwt_entropy_log_dec_12 | -39.634 | 0.239 | -39.390 | 0.240 | 8.87e-01 | 0.770 | 0.770 | NA | |
| La_tqwt_entropy_log_dec_28 | + 1.000tqwt_entropy_log_dec_28 -0.981tqwt_entropy_log_dec_29 | -0.633 | 0.430 | -0.819 | 0.273 | 1.25e-07 | 0.758 | 0.654 | -1 |
| La_std_2nd_delta | -0.907std_MFCC_2nd_coef + 1.000std_2nd_delta | 0.462 | 0.132 | 0.329 | 0.144 | 7.54e-01 | 0.754 | 0.630 | 0 |
| mean_MFCC_2nd_coef | 21.360 | 18.112 | 1.716 | 27.881 | 4.61e-07 | 0.753 | 0.753 | NA | |
| La_tqwt_energy_dec_33 | -0.919tqwt_energy_dec_32 + 1.000tqwt_energy_dec_33 | 0.745 | 0.372 | 1.217 | 0.680 | 8.01e-01 | 0.736 | 0.509 | 1 |
| La_tqwt_kurtosisValue_dec_33 | -0.788tqwt_kurtosisValue_dec_31 + 1.000tqwt_kurtosisValue_dec_33 | 6.360 | 0.407 | 5.975 | 0.553 | 1.62e-01 | 0.736 | 0.628 | -1 |
| tqwt_kurtosisValue_dec_18 | 28.598 | 0.288 | 28.395 | 0.144 | 9.92e-01 | 0.734 | 0.734 | 3 | |
| La_apq11Shimmer | -0.907locShimmer + 1.000apq11Shimmer | 2.150 | 0.161 | 2.031 | 0.133 | 4.19e-01 | 0.734 | 0.713 | -1 |
| apq11Shimmer | NA | 24.713 | 0.452 | 24.313 | 0.543 | 7.01e-01 | 0.713 | 0.713 | NA |
| locShimmer | NA | 24.873 | 0.487 | 24.564 | 0.583 | 9.78e-01 | 0.663 | 0.663 | 4 |
| tqwt_entropy_log_dec_28 | NA | -36.009 | 5.040 | -36.613 | 0.482 | 3.82e-03 | 0.654 | 0.654 | NA |
| std_2nd_delta | NA | 24.798 | 0.314 | 24.656 | 0.285 | 4.43e-01 | 0.630 | 0.630 | NA |
| tqwt_kurtosisValue_dec_33 | NA | 29.796 | 0.904 | 29.434 | 0.819 | 5.74e-02 | 0.628 | 0.628 | NA |
| tqwt_entropy_log_dec_29 | NA | -36.051 | 5.123 | -36.477 | 0.317 | 9.23e-03 | 0.565 | 0.565 | NA |
| tqwt_energy_dec_32 | NA | 18.264 | 1.945 | 18.116 | 2.434 | 2.91e-01 | 0.546 | 0.546 | 3 |
| tqwt_energy_dec_33 | NA | 17.527 | 1.794 | 17.864 | 2.429 | 2.00e-01 | 0.509 | 0.509 | NA |
| std_MFCC_2nd_coef | NA | 26.823 | 0.313 | 26.812 | 0.280 | 7.85e-01 | 0.508 | 0.508 | 1 |
| tqwt_kurtosisValue_dec_31 | NA | 29.740 | 0.943 | 29.769 | 1.020 | 1.36e-01 | 0.490 | 0.490 | 3 |
featuresnames <- colnames(dataframe)[colnames(dataframe) != outcome]
pc <- prcomp(dataframe[,iscontinous],center = TRUE,tol=0.002) #principal components
predPCA <- predict(pc,dataframe[,iscontinous])
PCAdataframe <- as.data.frame(cbind(predPCA,dataframe[,!iscontinous]))
colnames(PCAdataframe) <- c(colnames(predPCA),colnames(dataframe)[!iscontinous])
#plot(PCAdataframe[,colnames(PCAdataframe)!=outcome],col=dataframe[,outcome],cex=0.65,cex.lab=0.5,cex.axis=0.75,cex.sub=0.5,cex.main=0.75)
#pander::pander(pc$rotation)
PCACor <- cor(PCAdataframe[,colnames(PCAdataframe) != outcome])
gplots::heatmap.2(abs(PCACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "PCA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
EFAdataframe <- dataframeScaled
if (length(iscontinous) < 2000)
{
topred <- min(length(iscontinous),nrow(dataframeScaled),ncol(predPCA)/2)
if (topred < 2) topred <- 2
uls <- fa(dataframeScaled[,iscontinous],nfactors=topred,rotate="varimax",warnings=FALSE) # EFA analysis
predEFA <- predict(uls,dataframeScaled[,iscontinous])
EFAdataframe <- as.data.frame(cbind(predEFA,dataframeScaled[,!iscontinous]))
colnames(EFAdataframe) <- c(colnames(predEFA),colnames(dataframeScaled)[!iscontinous])
EFACor <- cor(EFAdataframe[,colnames(EFAdataframe) != outcome])
gplots::heatmap.2(abs(EFACor),
trace = "none",
# scale = "row",
mar = c(5,5),
col=rev(heat.colors(5)),
main = "EFA Correlation",
cexRow = 0.5,
cexCol = 0.5,
srtCol=45,
srtRow= -45,
key.title=NA,
key.xlab="Pearson Correlation",
xlab="Feature", ylab="Feature")
}
par(op)
par(xpd = TRUE)
dataframe[,outcome] <- factor(dataframe[,outcome])
rawmodel <- rpart(paste(outcome,"~."),dataframe,control=rpart.control(maxdepth=3))
pr <- predict(rawmodel,dataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(rawmodel,main="Raw",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(rawmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,dataframe[,outcome]==0))
}
pander::pander(table(dataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 39 | 25 |
| 1 | 3 | 185 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.8333 | 0.78147 | 0.8772 |
| tp | 0.7460 | 0.68760 | 0.7986 |
| se | 0.9840 | 0.95408 | 0.9967 |
| sp | 0.6094 | 0.47932 | 0.7290 |
| diag.ac | 0.8889 | 0.84343 | 0.9249 |
| diag.or | 96.2000 | 27.66232 | 334.5503 |
| nndx | 1.6852 | 1.37805 | 2.3074 |
| youden | 0.5934 | 0.43339 | 0.7257 |
| pv.pos | 0.8810 | 0.82929 | 0.9215 |
| pv.neg | 0.9286 | 0.80517 | 0.9850 |
| lr.pos | 2.5191 | 1.85407 | 3.4228 |
| lr.neg | 0.0262 | 0.00838 | 0.0818 |
| p.rout | 0.1667 | 0.12284 | 0.2185 |
| p.rin | 0.8333 | 0.78147 | 0.8772 |
| p.tpdn | 0.3906 | 0.27104 | 0.5207 |
| p.tndp | 0.0160 | 0.00330 | 0.0459 |
| p.dntp | 0.1190 | 0.07854 | 0.1707 |
| p.dptn | 0.0714 | 0.01498 | 0.1948 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 185 | 25 | 210 |
| Test - | 3 | 39 | 42 |
| Total | 188 | 64 | 252 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.889 | 0.843 | 0.925 |
| 3 | se | 0.984 | 0.954 | 0.997 |
| 4 | sp | 0.609 | 0.479 | 0.729 |
| 6 | diag.or | 96.200 | 27.662 | 334.550 |
par(op)
par(xpd = TRUE)
DEdataframe[,outcome] <- factor(DEdataframe[,outcome])
IDeAmodel <- rpart(paste(outcome,"~."),DEdataframe,control=rpart.control(maxdepth=3))
pr <- predict(IDeAmodel,DEdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(IDeAmodel,main="IDeA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(IDeAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,DEdataframe[,outcome]==0))
}
pander::pander(table(DEdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 46 | 18 |
| 1 | 6 | 182 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.7937 | 0.7384 | 0.8419 |
| tp | 0.7460 | 0.6876 | 0.7986 |
| se | 0.9681 | 0.9318 | 0.9882 |
| sp | 0.7188 | 0.5924 | 0.8240 |
| diag.ac | 0.9048 | 0.8616 | 0.9380 |
| diag.or | 77.5185 | 29.1251 | 206.3207 |
| nndx | 1.4560 | 1.2312 | 1.9076 |
| youden | 0.6868 | 0.5242 | 0.8122 |
| pv.pos | 0.9100 | 0.8615 | 0.9458 |
| pv.neg | 0.8846 | 0.7656 | 0.9565 |
| lr.pos | 3.4421 | 2.3246 | 5.0967 |
| lr.neg | 0.0444 | 0.0199 | 0.0990 |
| p.rout | 0.2063 | 0.1581 | 0.2616 |
| p.rin | 0.7937 | 0.7384 | 0.8419 |
| p.tpdn | 0.2812 | 0.1760 | 0.4076 |
| p.tndp | 0.0319 | 0.0118 | 0.0682 |
| p.dntp | 0.0900 | 0.0542 | 0.1385 |
| p.dptn | 0.1154 | 0.0435 | 0.2344 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 182 | 18 | 200 |
| Test - | 6 | 46 | 52 |
| Total | 188 | 64 | 252 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.905 | 0.862 | 0.938 |
| 3 | se | 0.968 | 0.932 | 0.988 |
| 4 | sp | 0.719 | 0.592 | 0.824 |
| 6 | diag.or | 77.519 | 29.125 | 206.321 |
par(op)
par(xpd = TRUE)
PCAdataframe[,outcome] <- factor(PCAdataframe[,outcome])
PCAmodel <- rpart(paste(outcome,"~."),PCAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(PCAmodel,PCAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(PCAmodel,main="PCA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(PCAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,PCAdataframe[,outcome]==0))
}
pander::pander(table(PCAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 31 | 33 |
| 1 | 7 | 181 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.8492 | 0.7989 | 0.8910 |
| tp | 0.7460 | 0.6876 | 0.7986 |
| se | 0.9628 | 0.9248 | 0.9849 |
| sp | 0.4844 | 0.3575 | 0.6127 |
| diag.ac | 0.8413 | 0.7902 | 0.8841 |
| diag.or | 24.2900 | 9.8738 | 59.7548 |
| nndx | 2.2364 | 1.6732 | 3.5424 |
| youden | 0.4471 | 0.2823 | 0.5976 |
| pv.pos | 0.8458 | 0.7903 | 0.8914 |
| pv.neg | 0.8158 | 0.6567 | 0.9226 |
| lr.pos | 1.8672 | 1.4701 | 2.3716 |
| lr.neg | 0.0769 | 0.0356 | 0.1660 |
| p.rout | 0.1508 | 0.1090 | 0.2011 |
| p.rin | 0.8492 | 0.7989 | 0.8910 |
| p.tpdn | 0.5156 | 0.3873 | 0.6425 |
| p.tndp | 0.0372 | 0.0151 | 0.0752 |
| p.dntp | 0.1542 | 0.1086 | 0.2097 |
| p.dptn | 0.1842 | 0.0774 | 0.3433 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 181 | 33 | 214 |
| Test - | 7 | 31 | 38 |
| Total | 188 | 64 | 252 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.841 | 0.790 | 0.884 |
| 3 | se | 0.963 | 0.925 | 0.985 |
| 4 | sp | 0.484 | 0.358 | 0.613 |
| 6 | diag.or | 24.290 | 9.874 | 59.755 |
par(op)
EFAdataframe[,outcome] <- factor(EFAdataframe[,outcome])
EFAmodel <- rpart(paste(outcome,"~."),EFAdataframe,control=rpart.control(maxdepth=3))
pr <- predict(EFAmodel,EFAdataframe,type = "class")
ptab <- list(er="Error",detail=matrix(nrow=6,ncol=1))
if (length(unique(pr))>1)
{
plot(EFAmodel,main="EFA",branch=0.5,uniform = TRUE,compress = TRUE,margin=0.1)
text(EFAmodel, use.n = TRUE,cex=0.75)
ptab <- epiR::epi.tests(table(pr==0,EFAdataframe[,outcome]==0))
}
pander::pander(table(EFAdataframe[,outcome],pr))
| 0 | 1 | |
|---|---|---|
| 0 | 45 | 19 |
| 1 | 12 | 176 |
pander::pander(ptab)
detail:
| statistic | est | lower | upper |
|---|---|---|---|
| ap | 0.7738 | 0.7171 | 0.824 |
| tp | 0.7460 | 0.6876 | 0.799 |
| se | 0.9362 | 0.8912 | 0.967 |
| sp | 0.7031 | 0.5758 | 0.811 |
| diag.ac | 0.8770 | 0.8300 | 0.915 |
| diag.or | 34.7368 | 15.7115 | 76.800 |
| nndx | 1.5642 | 1.2862 | 2.141 |
| youden | 0.6393 | 0.4670 | 0.777 |
| pv.pos | 0.9026 | 0.8520 | 0.940 |
| pv.neg | 0.7895 | 0.6611 | 0.886 |
| lr.pos | 3.1534 | 2.1589 | 4.606 |
| lr.neg | 0.0908 | 0.0513 | 0.161 |
| p.rout | 0.2262 | 0.1760 | 0.283 |
| p.rin | 0.7738 | 0.7171 | 0.824 |
| p.tpdn | 0.2969 | 0.1891 | 0.424 |
| p.tndp | 0.0638 | 0.0334 | 0.109 |
| p.dntp | 0.0974 | 0.0597 | 0.148 |
| p.dptn | 0.2105 | 0.1138 | 0.339 |
tab:
| Outcome + | Outcome - | Total | |
|---|---|---|---|
| Test + | 176 | 19 | 195 |
| Test - | 12 | 45 | 57 |
| Total | 188 | 64 | 252 |
method: exact
digits: 2
conf.level: 0.95
pander::pander(ptab$detail[c(5,3,4,6),])
| statistic | est | lower | upper | |
|---|---|---|---|---|
| 5 | diag.ac | 0.877 | 0.830 | 0.915 |
| 3 | se | 0.936 | 0.891 | 0.967 |
| 4 | sp | 0.703 | 0.576 | 0.811 |
| 6 | diag.or | 34.737 | 15.712 | 76.800 |
par(op)